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  • Technical Perspective: Checking Invariant Confluence, In Whole or In Parts
    ACM SIGMOD Rec. (IF 0.711) Pub Date : 2020-09-04
    Johannes Gehrke

    Never make a promise - you may have to keep it. - Neil Jordan Database systems were known to provide strong consistency guarantees. As an example, database textbook defines the ACID guarantees as "four important properties of transactions to maintain data in the face of concurrent access and system failures" [2]. Beyond atomicity, consistency, and durability, the "I" in ACID is loosely defined as "Users

    更新日期:2020-09-05
  • Checking Invariant Confluence, In Whole or In Parts
    ACM SIGMOD Rec. (IF 0.711) Pub Date : 2020-09-04
    Michael Whittaker; Joseph M. Hellerstein

    Strongly consistent distributed systems are easy to reason about but face fundamental limitations in availability and performance. Weakly consistent systems can be implemented with very high performance but place a burden on the application developer to reason about complex interleavings of execution. Invariant confluence provides a formal framework for understanding when we can get the best of both

    更新日期:2020-09-05
  • Technical Perspective of Concurrent Prefix Recovery: Performing CPR on a Database
    ACM SIGMOD Rec. (IF 0.711) Pub Date : 2020-09-04
    Philip A. Bernstein

    Where do novel database system research results come from? In the 1970's, most systems research papers proposed mechanisms to support abstractions that were being explored for the first time, such as data translation, indexing, query optimization, high performance transactions, distributed databases, heterogeneous databases, and replicated databases. Novelty was easy to come by. These abstractions

    更新日期:2020-09-05
  • Concurrent Prefix Recovery: Performing CPR on a Database
    ACM SIGMOD Rec. (IF 0.711) Pub Date : 2020-09-04
    Guna Prasaad; Badrish Chandramouli; Donald Kossmann

    This paper proposes a new recovery model based on group commit, called concurrent prefix recovery (CPR). CPR differs from traditional group commit implementations in two ways: (1) it provides a semantic description of committed operations, of the form "all operations until time ti from session i"; and (2) it uses asynchronous incremental checkpointing instead of a WAL to implement group commit in a

    更新日期:2020-09-05
  • Technical Perspective: Constant-Delay Enumeration for Nondeterministic Document Spanners
    ACM SIGMOD Rec. (IF 0.711) Pub Date : 2020-09-04
    Benny Kimelfeld

    The challenge of extracting structured information from text, or sequential data in general, is prevalent across a multitude of data-science domains. This challenge, known as Information Extraction (IE), instantiates to core components in text analytics, and a plethora of IE paradigms have been developed over the past decades. Rules and rule systems have consistently been key components in such paradigms

    更新日期:2020-09-05
  • Constant-Delay Enumeration for Nondeterministic Document Spanners
    ACM SIGMOD Rec. (IF 0.711) Pub Date : 2020-09-04
    Antoine Amarilli; Pierre Bourhis; Stefan Mengel; Matthias Niewerth

    One of the classical tasks in information extraction is to extract subparts of texts through regular expressions. In the database theory literature, this approach has been generalized and formalized as document spanners. In this model, extraction is performed by evaluating a particular kind of automata, called a sequential variable-set automaton (VA). The efficiency of this task is then measured in

    更新日期:2020-09-05
  • Technical Perspective: Database Repair Meets Algorithmic Fairness
    ACM SIGMOD Rec. (IF 0.711) Pub Date : 2020-09-04
    Lise Getoor

    There has been an explosion of interest in fairness in machine learning. In large part, this has been motivated by societal issues highlighted in a string of well publicized cases such as gender biased job recommendation and racially biased criminal risk prediction algorithms. Both the recognition of the potential disparate impacts of machine learning due to historical bias in the data and the realization

    更新日期:2020-09-05
  • Database Repair Meets Algorithmic Fairness
    ACM SIGMOD Rec. (IF 0.711) Pub Date : 2020-09-04
    Babak Salimi; Bill Howe; Dan Suciu

    Fairness is increasingly recognized as a critical component of machine learning systems. However, it is the underlying data on which these systems are trained that often reflect discrimination, suggesting a database repair problem. Existing treatments of fairness rely on statistical correlations that can be fooled by anomalies, such as Simpson's paradox. Proposals for causality-based definitions of

    更新日期:2020-09-05
  • Technical Perspective: Declarative Recursive Computation on an RDBMS
    ACM SIGMOD Rec. (IF 0.711) Pub Date : 2020-09-04
    Matthias Boehm

    From a historical perspective, relational database management systems (RDBMSs) have integrated many specialized systems and data models back into the RDBMS over time. New workloads motivated specialized systems for performance, but over time, general-purpose RDBMSs absorbed this functionality to avoid boundary crossing. We already witnessed this process for object-relational functionality, XML and

    更新日期:2020-09-05
  • Declarative Recursive Computation on an RDBMS: or, Why You Should Use a Database For Distributed Machine Learning
    ACM SIGMOD Rec. (IF 0.711) Pub Date : 2020-09-04
    Dimitrije Jankov; Shangyu Luo; Binhang Yuan; Zhuhua Cai; Jia Zou; Chris Jermaine; Zekai J. Gao

    We explore the close relationship between the tensor-based computations performed during modern machine learning, and relational database computations. We consider how to make a very small set of changes to a modern RDBMS to make it suitable for distributed learning computations. Changes include adding better support for recursion, and optimization and execution of very large compute plans. We also

    更新日期:2020-09-05
  • Technical Perspective: Efficient Logspace Classes for Enumeration, Counting, and Uniform Generation
    ACM SIGMOD Rec. (IF 0.711) Pub Date : 2020-09-04
    Reinhard Pichler

    Traditionally, by query answering we mean the problem of finding all answers to a given query over a given database. But what happens if the number of answers is prohibitively big - which may easily occur in a Big Data context? In such situations, it seems preferable to have a mechanism that produces one answer after the other with certain guarantees on the time between any two outputs and to let the

    更新日期:2020-09-05
  • Efficient Logspace Classes for Enumeration, Counting, and Uniform Generation
    ACM SIGMOD Rec. (IF 0.711) Pub Date : 2020-09-04
    Marcelo Arenas; Luis Alberto Croquevielle; Rajesh Jayaram; Cristian Riveros

    We study two simple yet general complexity classes, which provide a unifying framework for efficient query evaluation in areas like graph databases and information extraction, among others. We investigate the complexity of three fundamental algorithmic problems for these classes: enumeration, counting and uniform generation of solutions, and show that they have several desirable properties in this

    更新日期:2020-09-05
  • Technical Perspective: Query Optimization for Faster Deep CNN Explanations
    ACM SIGMOD Rec. (IF 0.711) Pub Date : 2020-09-04
    Sebastian Schelter

    Machine learning (ML) is increasingly used to automate decision making in various domains. In recent years, ML has not only been applied to tasks that use structured input data, but also, tasks that operate on data with less strictly defined structure such as speech, images and videos. Prominent examples are speech recognition for personal assistants or face recognition for boarding airplanes.

    更新日期:2020-09-05
  • Query Optimization for Faster Deep CNN Explanations
    ACM SIGMOD Rec. (IF 0.711) Pub Date : 2020-09-04
    Supun Nakandala; Arun Kumar; Yannis Papakonstantinou

    Deep Convolutional Neural Networks (CNNs) now match human accuracy in many image prediction tasks, resulting in a growing adoption in e-commerce, radiology, and other domains. Naturally, "explaining" CNN predictions is a key concern for many users. Since the internal workings of CNNs are unintuitive for most users, occlusion-based explanations (OBE) are popular for understanding which parts of an image

    更新日期:2020-09-05
  • Technical Perspective: Revealing Every Story of Data in Blockchain Systems
    ACM SIGMOD Rec. (IF 0.711) Pub Date : 2020-09-04
    Yaron Kanza

    For many applications, data are worthy only if they are trustworthy. The concept of trust is sometimes elusive, and yet it is fundamental in data management. Even when not expressed explicitly, the correctness of computations and reliability of applications depend on trustworthy management of the data. These notions received new attention with the advent of blockchain and distributed ledger technology

    更新日期:2020-09-05
  • Revealing Every Story of Data in Blockchain Systems
    ACM SIGMOD Rec. (IF 0.711) Pub Date : 2020-09-04
    Pingcheng Ruan; Tien Tuan Anh Dinh; Qian Lin; Meihui Zhang; Gang Chen; Beng Chin Ooi

    The success of Bitcoin and other cryptocurrencies bring enormous interest to blockchains. A blockchain system implements a tamper-evident ledger for recording transactions that modify some global states. The system captures the entire evolution history of the states. The management of that history, also known as data provenance or lineage, has been studied extensively in database systems. However,

    更新日期:2020-09-05
  • Limit Datalog: A Declarative Query Language for Data Analysis
    ACM SIGMOD Rec. (IF 0.711) Pub Date : 2020-02-25
    Bernardo Cuenca Grau; Ian Horrocks; Mark Kaminski; Egor V. Kostylev; Boris Motik

    Motivated by applications in declarative data analysis, we study DatalogZ-an extension of Datalog with stratified negation and arithmetics over integers. Reasoning in this language is undecidable, so we present a fragment, called limit DatalogZ, that is powerful enough to naturally capture many important data analysis tasks. In limit DatalogZ, all intensional predicates with a numeric argument are

    更新日期:2020-02-25
  • Hardware-Conscious Stream Processing: A Survey
    ACM SIGMOD Rec. (IF 0.711) Pub Date : 2020-02-25
    Shuhao Zhang; Feng Zhang; Yingjun Wu; Bingsheng He; Paul Johns

    Data stream processing systems (DSPSs) enable users to express and run stream applications to continuously process data streams. To achieve realtime data analytics, recent researches keep focusing on optimizing the system latency and throughput. Witnessing the recent great achievements in the computer architecture community, researchers and practitioners have investigated the potential of adoption

    更新日期:2020-02-25
  • Domain- and Structure-Agnostic End-to-End Entity Resolution with JedAI
    ACM SIGMOD Rec. (IF 0.711) Pub Date : 2020-02-25
    George Papadakis; Leonidas Tsekouras; Emmanouil Thanos; George Giannakopoulos; Themis Palpanas; Manolis Koubarakis

    We present JedAI, a new open-source toolkit for endto- end Entity Resolution. JedAI is domain-agnostic in the sense that it does not depend on background expert knowledge, applying seamlessly to data of any domain with minimal human intervention. JedAI is also structure-agnostic, as it can process any type of data, ranging from structured (relational) to semi-structured (RDF) and un-structured (free-text)

    更新日期:2020-02-25
  • Natassa Ailamaki Speaks Out on How to be a Systems Researcher and How to Manage a Large Research Group
    ACM SIGMOD Rec. (IF 0.711) Pub Date : 2020-02-25
    Marianne Winslett; Vanessa Braganholo

    Welcome to ACM SIGMOD Record's series of interviews with distinguished members of the database community. I'm Marianne Winslett, and today we're at the 2017 SIGMOD and PODS conference in Chicago. I have here with me Anastasia Ailamaki, who's a professor at the Swiss Federal Institute of Technology, better known as EPFL. Before that, Natassa was a professor at Carnegie Mellon. She's an ACM Fellow, a

    更新日期:2020-02-25
  • The Seattle Report on Database Research
    ACM SIGMOD Rec. (IF 0.711) Pub Date : 2020-02-25
    Daniel Abadi; Anastasia Ailamaki; David Andersen; Peter Bailis; Magdalena Balazinska; Philip Bernstein; Peter Boncz; Surajit Chaudhuri; Alvin Cheung; AnHai Doan; Luna Dong; Michael J. Franklin; Juliana Freire; Alon Halevy; Joseph M. Hellerstein; Stratos Idreos; Donald Kossmann; Tim Kraska; Sailesh Krishnamurthy; Volker Markl; Sergey Melnik; Tova Milo; C. Mohan; Thomas Neumann; Beng Chin Ooi; Fatma

    Approximately every five years, a group of database researchers meet to do a self-assessment of our community, including reflections on our impact on the industry as well as challenges facing our research community. This report summarizes the discussion and conclusions of the 9th such meeting, held during October 9-10, 2018 in Seattle.

    更新日期:2020-02-25
  • Digital Libraries: Supporting Open Science
    ACM SIGMOD Rec. (IF 0.711) Pub Date : 2020-02-25
    Paolo Manghi; Leonardo Candela; Emma Lazzeri; Gianmaria Silvello

    The Italian Research Conference on Digital Libraries (IRCDL) is the annual Italian forum to discuss research topics on Digital Libraries and related technical, practical, and social issues. Along the years, IRCDL touched several aspects underlying the ?Digital Library" domain and promptly adapted to the evolution of the field. Today, the ?Digital Library" field includes theory and practices reflecting

    更新日期:2020-02-25
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